Distant Metastasis After Chemoradiation and Image Guided Adaptive Brachytherapy in Locally Advanced Cervical Cancer
Bibliographic record
Abstract
PURPOSE: This study aimed to assess patterns and risks of distant metastasis (DM) in patients with cervical cancer treated with chemoradiation therapy and MR-image guided adaptive brachytherapy (IGABT) and to explore a potential dose-effect relationship of concomitant cisplatin. METHODS AND MATERIALS: ] stage IB-IVA or stage IVB limited to paraaortic lymph nodes below the L1/L2 interspace). Treatment involved external beam radiation therapy (45-50.4 Gy), weekly cisplatin (40 mg/m², 30 mg/m², or paused), and IGABT. DM was defined as extra-pelvic recurrence excluding paraaortic nodes. RESULTS: The analysis included 1318 patients with a median age of 49 years and a median follow-up of 52 months. The 5-year cumulative incidence of DM was 14%, with the lungs (26%), mediastinal lymph nodes (15%), and bones (10%) identified as the most common metastatic sites. Key risk factors for DM included nonsquamous histology (HR, 1.89; 95% CI, 1.30-2.75), nodal involvement at diagnosis (pelvic-only nodes: HR, 1.56; 95% CI, 1.07-2.26; paraaortic nodes: HR, 3.15; 95% CI, 1.93-5.16), and large target volume at brachytherapy (HR, 1.93; 95% CI, 1.21-3.08). Patients receiving fewer than 4 cycles of chemotherapy demonstrated a significantly higher risk of DM (HR, 1.52; 95% CI, 1.08-2.13). CONCLUSION: DM is a substantial burden in patients with locally advanced cervical cancer, with the lungs, distant lymph nodes, and bones being the most frequent sites. Risk factors such as nonsquamous histology, nodal involvement, and large target volumes at brachytherapy are critical considerations for identifying high-risk patients in future studies. These findings highlight the need for tailored strategies to mitigate DM in this patient population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".